Land-based seismic data acquisition and processing techniques are used to generate a profile (image) of a geophysical structure (subsurface) of the underlying strata. This profile does not necessarily provide an accurate location for fluid reservoirs, but it may suggest, to those trained in the field, the presence or absence of fluid reservoirs.
The acquisition of data in land-based seismic methods usually produces different results in source strength and signature based on differences in near-surface conditions. Further data processing and interpretation of seismic data requires correction of these differences in the early stages of processing. Surface consistent amplitude corrections may be used in seismic processing to correct for amplitude perturbations due to the near surface conditions. The performance of sources and receivers arrays is sensitive to the variations in the sources strength and receivers coupling. Conventional approaches to address these variations involve decomposing the root-mean-square (RMS) amplitude of each seismic trace from an array of sources and receivers into a combination of a source term As and a receiver term Ar as A=αAsAr. Conventional correction for surface-consistent amplitudes of P-P and P-S datasets from multicomponent surveys are performed separately leading to two distinct sets of corrective gains for the P-P and P-S traces.
The data from the P-P and P-S traces have different time scales due to the differences in propagations of these waves. In general, a P-wave is an elastic body wave in which particle motion is in the direction of propagation. By contrast, in general, an S-wave is an elastic body wave in which particles oscillate perpendicular to the direction in which the wave propagates. A gamma ratio may be used to compare the P-P and P-S datasets. The gamma ratios enables to draw a link between P-P and P-S time scales and offers the way to relocate converted events in the same time scale as the P-P events. Several techniques are available to estimate this key value, based on criteria like the optimal correlation between opposite azimuth stacks or derived from transit times. By nature, even after a stretching of the P-S dataset to P-P time, the P-S dataset amplitudes are different from the P-P datasets amplitudes. To jointly use the two datasets for the determination of surface consistent scalars for shots and receivers, the average amplitudes of the two datasets must be equivalent, which may require a global equalization gain. Afterwards, a common value can be used for the mean amplitude term in the surface consistent model.
Conventional systems and methods for surface consistent amplitude corrections perform separate processing on the P-P dataset and the P-S dataset. This requires two processing steps. In addition, the separate solution of the surface consistent amplitude correction of the P-P dataset does not inform the surface consistent amplitude correction of the P-S dataset. Nor does the separate solution of the surface consistent amplitude correction of the P-S dataset inform the surface consistent amplitude correction of the P-P dataset. What is needed therefore are methods and systems for performing a joined inversion of the P-P and P-S datasets for the determination of surface-consistent amplitude corrections.